Intelligent chronic kidney disease early screening instrument

Abstract

The invention relates to the technical field of chronic kidney disease screening, and discloses an intelligent chronic kidney disease early screening instrument which comprises a physiological parameter detection module configured to synchronously collect multi-dimensional bio-electricity signals, optical characteristic parameters and body fluid biochemical parameters of a subject and output original signals; the multi-modal data processing module is in communication connection with the physiological parameter detection module and is configured to receive original signals, perform time domain and frequency domain conjoint analysis and construct a parameter incidence matrix; the wireless communication module is configured to realize encrypted interaction between the screening data and the cloud server and block chain evidence storage; and the man-machine interaction module is configured to display the risk level map in real time. According to the method, holographic dynamic correlation modeling of kidney function related micro-electrophysiological fluctuation and optical metabolism characteristics is realized, and a specific cross-modal pathological correlation mode of early renal injury can be effectively identified based on a time-frequency domain combined characteristic extraction and dynamic coupling algorithm.

Description

Technical Field

[0001] This invention relates to the field of chronic kidney disease screening technology, specifically to an intelligent early screening device for chronic kidney disease. Background Technology

[0002] Early screening for chronic kidney disease (CKD) is crucial for slowing disease progression. Current screening technologies primarily rely on biomarkers such as creatinine and urinary protein, as well as monitoring single physiological parameters. However, due to the physiological compensatory mechanisms of early kidney damage, traditional detection methods suffer from insufficient sensitivity and low specificity, resulting in most patients being diagnosed at an advanced stage. Conventional screening equipment is limited by single-modal data acquisition and static threshold judgment mechanisms, failing to capture latent pathological features resulting from multi-system interactions.

[0003] In existing technologies, screening devices based on bioelectrical signals or optical sensors mostly adopt a time-division independent acquisition mode, lacking the ability to synchronously and dynamically correlate and analyze multi-dimensional physiological parameters. Their data processing methods are limited to feature extraction in a single dimension, such as time series or frequency domain, and fail to construct dynamic coupling models between cross-modal parameters, resulting in the clinical value of physiological variation signals not being effectively explored. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes an intelligent early screening instrument for chronic kidney disease, which solves the aforementioned technical problems.

[0005] Intelligent early screening device for chronic kidney disease includes: The physiological parameter detection module includes multiple sensors configured to simultaneously acquire multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters of the subject, and output raw signals containing multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters. The multimodal data processing module is communicatively connected to the physiological parameter detection module and is configured to receive raw signals, perform joint time-domain and frequency-domain analysis, and construct a parameter correlation matrix. The wireless communication module is configured to enable encrypted interaction between screening data and the cloud server, as well as blockchain-based evidence storage. The human-computer interaction module is configured to display the risk level map in real time.

[0006] Furthermore, the physiological parameter detection module includes: An optocoupler array configured to detect the surface temperature distribution and near-infrared spectral absorption characteristics of the renal region; A multi-channel bioimpedance analysis unit is configured to measure tissue conductivity using an AC excitation signal; The microfluidic chipset is configured to simultaneously detect the protein / creatinine ratio in urine samples.

[0007] Furthermore, the multi-channel bioimpedance analysis unit includes: A programmable frequency generator configured to output an AC excitation signal; The four-electrode detection circuit is electrically connected to the programmable frequency generator and uses automatic balancing bridge technology to eliminate the influence of contact impedance. A digital lock-in amplifier configured to extract impedance phase angle characteristics for a specified frequency band.

[0008] Furthermore, the multimodal data processing module includes: The dynamic baseline correction unit is configured to use a sliding window adaptive algorithm to eliminate signal drift caused by environmental factors. The multi-scale feature extraction unit is configured to perform wavelet packet decomposition and EMD empirical mode decomposition in parallel. The risk prediction engine is configured to generate a three-dimensional risk assessment matrix based on an interpretable machine learning model, a federated learning framework, and probabilistic statistical methods.

[0009] Furthermore, the risk prediction engine includes: The spatiotemporal attention mechanism submodule is configured to capture the nonlinear interaction relationships between different physiological parameters; The transfer learning framework is configured to pre-train model parameters using distributed data from multiple nodes in a federated learning framework through parameter aggregation. The uncertainty quantification unit is configured to calculate the confidence interval of the risk assessment results using the Monte Carlo method.

[0010] Furthermore, the human-computer interaction module includes: The augmented reality display unit is configured to overlay and present the spatial correspondence between the renal anatomical structure and abnormal indicators. The adaptive interface generation unit is configured to dynamically adjust the information presentation density based on the user's age and operating habits. A multilingual speech synthesis unit is configured to generate dietary and exercise recommendations in real time that conform to medical guidelines.

[0011] Furthermore, it also includes: The self-calibration module is configured to automatically perform sensor zero-point calibration and sensitivity verification before each test.

[0012] Furthermore, the self-calibration module includes: The temperature compensation unit is configured to use a thermistor to monitor the ambient temperature in real time. The photoelectric self-test unit is configured to verify the linear response characteristics of the optical channel through a built-in standard scatterer; The abnormal alarm unit is configured to automatically lock the monitoring function when the calibration deviation exceeds a preset threshold.

[0013] Furthermore, it also includes: A detachable sample collection component configured for aseptic collection and temporary storage of urine or blood samples.

[0014] Furthermore, the detachable sample collection component includes: Minimally invasive blood collection head, integrating a microneedle array with impedance feedback control; The intelligent mixing device is configured to automatically add anticoagulant. The QR code identification system is configured to establish a traceable link between sample information and test data through blockchain.

[0015] The invention employing the above technical solution has the following advantages: This invention overcomes the limitations of traditional single physiological parameter detection by simultaneously acquiring multi-dimensional bioelectrical signals, optical feature parameters, and body fluid biochemical parameters, as well as by fusing and analyzing multi-modal data. It achieves holographic dynamic correlation modeling of micro-electrophysiological fluctuations and optical metabolic features related to kidney function. Based on time-frequency domain joint feature extraction and dynamic coupling algorithm, it can effectively identify cross-modal pathological correlation patterns unique to early kidney injury. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a schematic diagram of the structure of the intelligent early screening instrument for chronic kidney disease of the present invention; Figure 2 This is a schematic diagram of the physiological parameter detection module in the intelligent early screening instrument for chronic kidney disease of the present invention; Figure 3 This is a schematic diagram of the multi-channel bioimpedance analysis unit in the intelligent early screening instrument for chronic kidney disease of the present invention; Figure 4 This is a schematic diagram of the multimodal data processing module in the intelligent early screening instrument for chronic kidney disease of the present invention; Figure 5 This is a schematic diagram of the human-computer interaction module in the intelligent early screening instrument for chronic kidney disease of the present invention; Figure 6 This is a schematic diagram of the self-calibration module in the intelligent early screening instrument for chronic kidney disease of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0020] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0021] like Figures 1-6 As shown, the intelligent early screening device for chronic kidney disease of the present invention includes: The physiological parameter detection module includes multiple sensors configured to simultaneously acquire multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters of the subject, and output raw signals containing multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters. The multimodal data processing module is connected to the physiological parameter detection module and is configured to receive raw signals, perform joint time-domain and frequency-domain analysis, and construct a parameter correlation matrix. The wireless communication module is configured to enable encrypted interaction between screening data and the cloud server, as well as blockchain-based evidence storage. The human-computer interaction module is configured to display the risk level map in real time.

[0022] Specifically, by simultaneously acquiring multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters, and by fusing and analyzing multi-modal data, the limitations of traditional physiological parameter detection are overcome. This enables holographic dynamic correlation modeling of renal function-related micro-electrophysiological fluctuations and optical metabolic characteristics. Furthermore, based on a dynamic coupling algorithm for time-domain and frequency-domain joint feature extraction, the unique cross-modal pathological correlation patterns of early renal injury can be effectively identified.

[0023] In this embodiment, the physiological parameter detection module includes: An optocoupler array configured to detect the surface temperature distribution and near-infrared spectral absorption characteristics of the renal region; A multi-channel bioimpedance analysis unit is configured to measure tissue conductivity using an AC excitation signal; The microfluidic chipset is configured to simultaneously detect the protein / creatinine ratio in urine samples.

[0024] In this embodiment, the multi-channel bioimpedance analysis unit includes: A programmable frequency generator configured to output an AC excitation signal; The four-electrode detection circuit is electrically connected to the programmable frequency generator and uses automatic balancing bridge technology to eliminate the influence of contact impedance. A digital lock-in amplifier configured to extract impedance phase angle characteristics for a specified frequency band.

[0025] Specifically, the optocoupler array consists of a 64-channel thermopile sensor and a 940mm dual-wavelength near-infrared light source; by scanning the surface of the kidney region, temperature gradient maps and dynamic changes in blood oxygen saturation are obtained.

[0026] Programmable frequency generator, outputting AC excitation signal with a frequency range of 1KHz-1MHz; The four-electrode detection circuit uses automatic balancing bridge technology to eliminate contact impedance and combines dual-frequency bioelectrical impedance analysis to measure the conductivity and dielectric constant of local kidney tissue. A digital lock-in amplifier, based on FPGA, implements fast Fourier transform to extract impedance phase angle feature values ​​for evaluating ion transport function of renal tubular epithelial cells.

[0027] The microfluidic chipset integrates a surface plasmon resonance sensor and a capacitive particle counter to separate albumin and creatinine in urine samples using microfluidic chromatography technology.

[0028] In this embodiment, the multimodal data processing module includes: The dynamic baseline correction unit is configured to use a sliding window adaptive algorithm to eliminate signal drift caused by environmental factors. The multi-scale feature extraction unit is configured to perform wavelet packet decomposition and EMD empirical mode decomposition in parallel. The risk prediction engine is configured to generate a three-dimensional risk assessment matrix based on an interpretable machine learning model, a federated learning framework, and probabilistic statistical methods.

[0029] Specifically, the dynamic baseline correction unit uses a sliding window adaptive algorithm to eliminate signal baseline drift caused by fluctuations in ambient temperature and humidity in real time. Multi-scale feature extraction unit, wavelet packet decomposition: Decomposes bioelectrical signals using wavelet basis functions; Empirical Mode Decomposition (EMD): Adaptive decomposition of optical parameters to obtain intrinsic mode functions that reflect the metabolic state of the kidney.

[0030] In this embodiment, the risk prediction engine includes: The spatiotemporal attention mechanism submodule is configured to capture the nonlinear interaction relationships between different physiological parameters; The transfer learning framework is configured to pre-train model parameters using distributed data from multiple nodes in a federated learning framework through parameter aggregation. The uncertainty quantification unit is configured to calculate the confidence interval of the risk assessment results using the Monte Carlo method.

[0031] Specifically, the spatiotemporal attention mechanism submodule constructs an encoder-decoder structure based on self-control attention mechanism and quantifies the cross-modal correlation weights between bioelectric signals and optical parameters; The transfer learning framework uses a pre-trained model based on a federated learning platform, and aggregates model parameters from various medical institutions through a differential privacy-preserving algorithm. The uncertainty quantification unit uses the Monte Carlo method to calculate the confidence interval of the estimated glomerular filtration rate, and triggers a review prompt when the interval width reaches a threshold. The final result is a three-dimensional risk assessment matrix that includes estimated glomerular filtration rate, urine protein grading, and vascular calcification index.

[0032] In this embodiment, the wireless communication module adopts a dual-mode transmission protocol, using Bluetooth and WIFI, and dynamically selects the optimal channel.

[0033] In this embodiment, the human-computer interaction module includes: The augmented reality display unit is configured to overlay and present the spatial correspondence between the renal anatomical structure and abnormal indicators. The adaptive interface generation unit is configured to dynamically adjust the information presentation density based on the user's age and operating habits. A multilingual speech synthesis unit is configured to generate dietary and exercise recommendations in real time that conform to medical guidelines.

[0034] Specifically, the augmented reality display unit overlays a three-dimensional anatomical model of the kidney onto the user's body surface projection using Simultaneous Localization and Mapping (SLAM) technology, marking the spatial location of abnormal parameters.

[0035] The adaptive interface generation unit is designed for elderly users, with the interface font size being enlarged and key indicators using red, yellow, and green color-coded warnings. For medical staff, it displays visual charts showing the original signal waveform and the feature extraction process.

[0036] The multilingual speech synthesis unit generates personalized suggestions based on the engine's analysis of risk assessment results.

[0037] In this embodiment, it also includes: The self-calibration module is configured to automatically perform sensor zero-point calibration and sensitivity verification before each test.

[0038] In this embodiment, the self-calibration module includes: The temperature compensation unit is configured to use a thermistor to monitor the ambient temperature in real time. The photoelectric self-test unit is configured to verify the linear response characteristics of the optical channel through a built-in standard scatterer; The abnormal alarm unit is configured to automatically lock the monitoring function when the calibration deviation exceeds a preset threshold.

[0039] Specifically, the temperature compensation unit uses a platinum resistance thermometer to monitor the ambient temperature in real time and performs polynomial compensation on the output of the optocoupler array. The photoelectric self-test unit drives the built-in standard scatterer to verify the linearity of the optical channel every time the device is powered on. The abnormal alarm unit automatically locks the detection function and displays a maintenance QR code when the calibration deviation exceeds the threshold.

[0040] In this embodiment, it also includes: A detachable sample collection component configured for aseptic collection and temporary storage of urine or blood samples.

[0041] In this embodiment, the detachable sample collection component includes: Minimally invasive blood collection head, integrating a microneedle array with impedance feedback control; The intelligent mixing device is configured to automatically add anticoagulant. The QR code identification system is configured to establish a traceable link between sample information and test data through blockchain.

[0042] Specifically, the minimally invasive blood collection head integrates a silicon-based microneedle array and controls the puncture depth through impedance feedback to ensure a stable blood collection volume; The intelligent mixing device has a built-in anticoagulant reservoir and automatically mixes samples at a 1:9 ratio according to sample volume. The QR code identification system encodes the sample ID, collection time, and operator information into a QR code, which is then bound to blockchain-stored evidence data, supporting full traceability and query.

[0043] Workflow: The user wears an optocoupler array on the surface of the kidney area to simultaneously collect bioimpedance signals and near-infrared spectra; The microfluidic chipset analyzes urine sample values ​​to complete the test; The multimodal data processing module integrates electrophysiological, optical, and biochemical data to generate a risk assessment matrix; The augmented reality interface marks abnormal areas of the kidneys and provides personalized health advice via voice. The test data is encrypted and uploaded to the cloud, where the blockchain generates an immutable electronic report.

[0044] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0045] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0046] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0047] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0048] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0049] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0050] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0051] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An intelligent early screening device for chronic kidney disease, characterized in that, include: The physiological parameter detection module includes multiple sensors configured to simultaneously acquire multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters of the subject, and output raw signals containing multi-dimensional bioelectrical signals, optical characteristic parameters, and body fluid biochemical parameters. The multimodal data processing module is communicatively connected to the physiological parameter detection module and is configured to receive raw signals, perform joint time-domain and frequency-domain analysis, and construct a parameter correlation matrix. The wireless communication module is configured to enable encrypted interaction between screening data and the cloud server, as well as blockchain-based evidence storage. The human-computer interaction module is configured to display the risk level map in real time.

2. The intelligent early screening instrument for chronic kidney disease according to claim 1, characterized in that, The physiological parameter detection module includes: An optocoupler array configured to detect the surface temperature distribution and near-infrared spectral absorption characteristics of the renal region; A multi-channel bioimpedance analysis unit is configured to measure tissue conductivity using an AC excitation signal; The microfluidic chipset is configured to simultaneously detect the protein / creatinine ratio in urine samples.

3. The intelligent early screening instrument for chronic kidney disease according to claim 2, characterized in that, The multi-channel bioimpedance analysis unit includes: A programmable frequency generator configured to output an AC excitation signal; The four-electrode detection circuit is electrically connected to the programmable frequency generator and uses automatic balancing bridge technology to eliminate the influence of contact impedance. A digital lock-in amplifier configured to extract impedance phase angle characteristics for a specified frequency band.

4. The intelligent early screening instrument for chronic kidney disease according to claim 1, characterized in that, The multimodal data processing module includes: The dynamic baseline correction unit is configured to use a sliding window adaptive algorithm to eliminate signal drift caused by environmental factors. The multi-scale feature extraction unit is configured to perform wavelet packet decomposition and EMD empirical mode decomposition in parallel. The risk prediction engine is configured to generate a three-dimensional risk assessment matrix based on an interpretable machine learning model, a federated learning framework, and probabilistic statistical methods.

5. The intelligent early screening instrument for chronic kidney disease according to claim 4, characterized in that, The risk prediction engine includes: The spatiotemporal attention mechanism submodule is configured to capture the nonlinear interaction relationships between different physiological parameters; The transfer learning framework is configured to pre-train model parameters using distributed data from multiple nodes in a federated learning framework through parameter aggregation. The uncertainty quantification unit is configured to calculate the confidence interval of the risk assessment results using the Monte Carlo method.

6. The intelligent early screening instrument for chronic kidney disease according to claim 1, characterized in that, The human-computer interaction module includes: The augmented reality display unit is configured to overlay and present the spatial correspondence between the renal anatomical structure and abnormal indicators. The adaptive interface generation unit is configured to dynamically adjust the information presentation density based on the user's age and operating habits. A multilingual speech synthesis unit is configured to generate dietary and exercise recommendations in real time that conform to medical guidelines.

7. The intelligent early screening instrument for chronic kidney disease according to claim 1, characterized in that, Also includes: The self-calibration module is configured to automatically perform sensor zero-point calibration and sensitivity verification before each test.

8. The intelligent early screening instrument for chronic kidney disease according to claim 7, characterized in that, The self-calibration module includes: The temperature compensation unit is configured to use a thermistor to monitor the ambient temperature in real time. The photoelectric self-test unit is configured to verify the linear response characteristics of the optical channel through a built-in standard scatterer; The abnormal alarm unit is configured to automatically lock the monitoring function when the calibration deviation exceeds a preset threshold.

9. The intelligent early screening device for chronic kidney disease according to any one of claims 1 to 8, characterized in that, Also includes: A detachable sample collection component configured for aseptic collection and temporary storage of urine or blood samples.

10. The intelligent early screening instrument for chronic kidney disease according to claim 9, characterized in that, The detachable sample collection component includes: Minimally invasive blood collection head, integrating a microneedle array with impedance feedback control; The intelligent mixing device is configured to automatically add anticoagulant. The QR code identification system is configured to establish a traceable link between sample information and test data through blockchain.

Images